Use of Spatial Analysis to Assess Geographic Accessibility of Community Pharmacies in São Mateus

Pharmaceutical services contribute to the rational use of medicines by ensuring the provision of appropriate, safe and effective pharmacotherapies. Thus, it is important to investigate whether the geographical distribution of pharmacies causes inequalities in access in countries like Brazil, where there is no federal law regulating the opening of new establishments (in contrast to many European countries). This study analyzed the accessibility of community pharmacies in the city of Sao Mateus, Espírito Santo, Brazil. The authors obtained the geographical coordinates of pharmacies and healthfacilities, which scanned and georeferenced the study area. The 40 pharmacies included in the spatial analysis presented a heterogeneous distribution, with the existence of neighborhoods with an accumulation of pharmacies and others with no pharmacies. The authors found that only 43.7% of the study area received pharmaceutical service coverage, when buffer zones of 350 meters are incorporated, and that most of the districts have a minimum distance of 100 meters between health facilities and pharmacies. It was concluded that the use of GIS proved to be very suitable for analysis of pharmaceutical services, enabling knowledge of the local situation and serving as a stimulus for future studies.

Root Growth, Nutrition and Yield of Maize with Applied Different Limestone Particle Size in the Cerrado Soil

The limestone with smaller particle size provides rapid correction of soil acidity. However, the limestone with large particles can promote bigger root growth, and may still have a residual effect on the soil. The aim of this study was to evaluate the residual effect of the application and incorporation of different limestone with a particle size bigger than 0.30 mm in root growth, nutrition and yield of maize in Cerrado soil. A randomized completed block utilized in this experiment, with five treatments and four replications. The grain sizes of dolomitic limestone incorporated into the Oxisol were: 0 (control, no-lime), 2.00-0.82 mm, 0.82-0.56 mm, 0.56-0.30 mm, 0.30-0.20 mm, in doses of 6.5 t·ha-1, 3.9 t·ha-1, 2.6 t·ha-1 and 1.3 t·ha-1 respectively. The limestone applied in introduction no-tillage. The maize were evaluated of root attributes, nutrition and grain yield in two years 2009 and 2010. The use of limestone particle size of 2.00-0.82 mm, and the quantity of 6.5 t·ha-1 applied to the soil and incorporated showed elongation and root growth of maize thin depth of 20-40 cm in the soil. In that, same proposed management of the limestone was the highest maize grain yield in the first year. The rooting characteristics evaluated correlated with leaf Ca and Cu and maizegrain yield.

Root Growth, Nutrition and Yield of Maize with Applied Finely Limestone in Surface of Cerrado Soil

The surface applies lime to correcting soil acidity in no-tillage and promotes root development and yield of maize. However, limestone with a particle size smaller than 0.30 mm cannot promote this effect. The aim of this study was to determine the root attributes, nutrition and productivity of maize with limestone ground in no-tillage. The experimental design was randomized blocks with five treatments and four replications. The treatments consisted of different particle sizes of limestone applying superficially to the soil under NT 0) control, without lime, 1) 0.30 mm to 0.20 mm 2) 0.20 mm to 0.10 mm, 3) 0, 10 mm to 0.05 mm 4) 0.05 mm to 0.01 mm. Attributes root, leaf nutrition and grain yield of maize were evaluated in two years 2009 and 2010. The elongation of roots in a 20-40 cm depth was greater when using limestone with particle size between 0.30 mm and 0.20 mm. The root length of maize plants correlated with the levels of Mn in maize leaves. The root development promoted by liming effect was not reflected in the productivity of maize.

t-SNE:A study on reducing the dimensionality of hyperspectral data for the regression problem of estimating oenological parameters

In recent years there is a growing importance in using machine learning techniques to improve procedures in precision agriculture:in this work we perform a study on models capable of predicting oenological parameters from hyperspectral images of wine grape berries,a specially relevant topic to boost production tasks for winemakers.Specifically,we explore the capabilities of a novel technique mostly used for visualization,t-Distributed Stochastic Neighbor Embedding(t-SNE),for reducing the dimensionality of the highly complex hyperspectral data and compare its performance with Principal Component Analysis(PCA)method,which despite the introduction of many nonlinear dimensionality reduction techniques over the years,had achieved the best results for real-world data across several studies in literature.Additionally we explore the potential of Kernel t-SNE,an extension to the t-SNE method that allows for the usage of the technique in streaming data or online scenarios.Our results show that,in a direct comparison,t-SNE achieves better metrics than PCA for most of the data sets in this work and that the regressor(Support Vector Regression,SVR)performs better with the t-SNE reduced features as inputs,accomplishing better predictions with lower error rates.Comparing the results with current literature,our shallow learning model paired with t-SNE achieves either better or on par results than those reported,even competing with more advanced models that use deep learning techniques,which should propel the introduction of t-SNE in more studies that require dimensionality reduction.